Sustainable Water Resource Management: Challenges and Opportunities

1. Introduction

Water is a basic human necessity, and the amount of water on Earth remains fairly constant [1,2]. As global population growth and economic development continues to expand, water demand continues to rise [3,4]. This creates mounting pressure between water supply and growing demand [5], making sustainable water resource management increasingly critical. Hydrological models serve as the primary tool for sustainable water resource management, providing a practical way to explore ‘what if’ scenarios where direct experimentation on watersheds or river systems is not feasible [6,7]. Hydrological models are typically classified into three categories based on their mathematical approach: empirical, conceptual, and physically based models [8]. In recent decades, the rapid development of artificial intelligence has led to the emergence of a fourth category of hydrological models known as machine learning models [9,10]. Unlike traditional empirical approaches, these machine learning models can learn complex nonlinear relationships from high-dimensional datasets without relying on predefined mathematical equations [11]. They have been widely applied to rainfall–runoff modeling [12,13], evapotranspiration modeling [14,15], groundwater–surface water interactions [16,17], and many other hydrological processes. This development reflects the field’s evolution toward more interdisciplinary approaches.

As hydrological modeling and water resource management continue to advance, they face emerging and increasingly complex challenges that require innovative, interdisciplinary solutions. This Special Issue, “Hydrological Modeling and Sustainable Water Resources Management”, explores several of these pressing challenges, organizing them into four major categories: (1) prediction and forecasting challenges, (2) solutions to data gaps, (3) water system performance and optimization, and (4) integrated impact assessment. Prediction and forecasting challenges primarily involve enhancing model performance under a wide range of conditions. One of the most difficult tasks is predicting extreme events such as floods and droughts, due to the inherently complex and highly nonlinear behavior of hydrological systems [18]. Additional uncertainties arise from model structure, parameter selection, and initial conditions, which further complicate accurate prediction and forecasting. Data gaps refer to limitations in data availability that hinder model development and highlight the need to address missing, rare, or outdated information relevant to a given system. Furthermore, water systems face specific environmental challenges, where improving the design and operation of these systems to maximize effectiveness while minimizing environmental and economic costs remains a significant concern. Lastly, water systems are influenced by a wide array of factors, with climate change being one of the most significant drivers. Understanding how various stressors interact simultaneously and developing frameworks to analyze and manage these complex interactions continues to be a major challenge. This Special Issue includes 14 original research articles and three reviews and explores solutions related to the four key challenges in achieving sustainable water resource management.

2. An Overview of Published Articles

The following brief overview serves only as a guide to these published articles; we encourage readers to explore the full texts according to their interests.

2.1. Prediction and Forecasting Challenges

The first article by Piraei et al. (contribution 1) develops machine learning approaches to predict drought conditions using the standardized precipitation index (SPI), exploring the accurate drought prediction across different climate zones with varied lag times.

The contribution by Larance et al. (contribution 2) develops a SARIMA model to forecast dissolved oxygen and water temperature in the Athabasca River, achieving strong performance (R² = 0.67–0.97) and providing a practical tool for predicting low dissolved oxygen events that threaten aquatic ecosystems.

The third article by Lee et al. (contribution 3) integrates SWAT with machine learning to predict specified alpha factors, improving baseflow simulation accuracy and providing a web-based tool for practical watershed management applications.

The systems review by Uddameri et al. (contribution 4) examines machine learning applications across the entire flood management spectrum, finding that conventional approaches often outperform deep learning methods for flood hazard modeling, while emerging technologies like satellite data and explainable AI offer promising research directions.

2.2. Data Gaps

The research by Villanueva et al. (contribution 5) develops a cost-effective method to create digital elevation models using aerial photogrammetry with acceptable accuracy trade-offs, providing an accessible alternative to expensive surveys in data-scarce regions.

In the fifth article, Yan et al. (contribution 6) apply the Budyko framework for actual evapotranspiration estimation in Ontario using remote sensing and single-parameter calibration, achieving good performance (NSE = 0.74) and providing an alternative to data-intensive traditional methods.

The article by Wang et al. (contribution 7) develops methods to monitor small water bodies using aerial images, solving the problem of undocumented or outdated information on small freshwater resources.

Pascual et al. (contribution 8) examine cultural and socio-economic relationships on spring ecohydrogeology in Mediterranean climate zones through a global literature review and case studies, addressing the lack of comprehensive understanding of cultural–ecological interactions.

2.3. Water System Performance and Optimization

The article by Coronado-Hernández et al. (contribution 9) develops a simplified method for computing extreme hydrographs in dam design using frequency analysis data. The approach achieved 11.9% validation error at a real hydropower plant, providing engineers with a more efficient tool for designing critical dam infrastructure.

The research from Assar et al. (contribution 10) compares computational methods for predicting backwater depth at bridge constrictions, finding that energy and momentum methods provide the most accurate results while WSPRO overestimates and Yarnell’s method underestimates afflux values.

Umukiza et al. (contribution 11) develop GIS-based methods to identify optimal dam locations, addressing the failure of existing poorly sited dams that dry out during drought periods.

The article by Doro (contribution 12) develops algorithms for simulating drainage water recycling systems using the APEX model. The model accurately predicted sediment loss reduction.

Razack et al. (contribution 13) assess Djibouti’s Dalha aquifer using numerical modeling and find current extraction is already unsustainable, with declining water tables projected under climate change scenarios, making any increased exploitation unfeasible despite growing regional demands.

2.4. Integrated Impact Assessment

The research by Hibbs (contribution 14) models groundwater flow in the transboundary Southeastern Hueco Aquifer, finding current recharge (3.5 mm/year) is insufficient compared to historical levels (10.4 mm/year).

The work of Grosser et al. (contribution 15) models climate change impacts on Germany’s Gersprenz catchment through 2100, projecting severe discharge reductions (85% summer, 38% autumn) and excessive water temperatures, while providing a framework for spatially targeted adaptation strategies.

The contribution by Kimbi et al. (contribution 16) uses SWAT modeling to assess 30-year urbanization impacts on a Japanese catchment, finding land use change caused a 34.9% groundwater recharge reduction and recommending sustainable management practices to balance urban development with water sustainability.

The final contribution by Gervasio et al. (contribution 17) examines climate change effects on Po River denitrification through sediment core experiments, finding that increased temperatures and nitrate enhance nitrogen removal processes, potentially reducing coastal eutrophication risks during summer.